The purpose of this work is to implement a software capable of predicting calls made in cellular networks in the city of Milan. The sources of traffic data are provided by the city's Italia Telecom generated by its users as well as by users
displaced to this area. Implementation is possible with different Deep Learning techniques and the TensorFlow framework and, to achieve this with sufficent prediction horizon, use of an LSTM network is advisable, as these networks have large long-term Memory. In the tests performed, the LSTM network was compared with a multilayer
perceptron. As expected, LSTM network showed the best performance. Based on the product developed in this work, the activation/deactivation of frequency bands in Frequency Shift Repeaters (FSR) could be improved when these are going to carry a higher number of calls than usual. As a result, we have been able to make predictions with a prediction horizon of 18 samples, which with the time interval of the data used is equivalent to three hours of prediction. This is a sufficient horizon to satisfy the proposed application.